DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/8/2026 has been entered.
Status of Claims
The following is an Office action in response to the communication filed 1/8/2026.
Claims 1-5 and 7-20 have been amended.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statement received 6/17/2025 has been reviewed and considered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s amendments and associated arguments, filed 1/8/26, with respect to the rejection of claims 15-20 under 35 U.S.C. §101 have been considered and are persuasive. The associated rejection has been withdrawn.
Applicant’s amendments and associated arguments, filed 1/8/26, with respect to the rejection of claims 1-20 under 35 U.S.C. §101 have been considered but they are not persuasive.
Applicant argues that the application proposes a specific, computer-implemented solution that improves AR/e- commerce systems by technically enforcing a persistent, machine-level correlation between an observed in-store item and its associated vendor. Examiner respectfully disagrees. As currently recited, the claimed invention is directed to a solution to a business problem (losing customers to competitors), where the competitor has an online presence. The identification of an item and a location based on an image, collecting that identified information, receiving a request (abstract) and transmitting information in response to that request (post-solution) does not amount to an improvement to a technical field (i.e. the claims do not recite an improvement to object recognition technologies, data processing structures, etc.). The recited sensors are utilized in their ordinary capacity (to capture information) and the collection and transmission of information amounts to technical operations that are both generic and well-known. The model, as recited, is abstract and the characterization of the model as virtual merely characterizes the field of use when recited at this level of breadth (requiring only the captured image and fulfillment information, which a human can achieve with pen and paper). Examiner notes that, in Enfish, LLC v. Microsoft Corp. (Fed Cir. 2016), both the claims and their supporting specification were directed to a self-referential table that functions differently than conventional database structures to achieve faster search times, and smaller memory requirements, thus achieving an improvement to pre-existing technology. Unlike the circumstances in Enfish, LLC v. Microsoft Corp. (Fed Cir. 2016), the focus of the claims is on a process that qualifies as an abstract idea for which computers are invoked merely as a tool, rather than on a specific implementation of a solution to a problem in the software arts. In summary, the claims recite abstract concepts applied using generic computing functionality.
Applicant further argues that the claims do not merely bolt an abstract correlation on top of a generic computer, but requires a particular sequence of inter-related technical operations that meaningfully tie any alleged abstract idea to tangible, technological improvements, and concretely changes how the networked computer system processes and structures outgoing purchase requests (i.e., instead of a generic order, the system generates a modified, enriched request that carries vendor attribution metadata derived from the earlier sensor- and image-based correlation, altering the content of network messages and the data in downstream transaction systems). Examiner respectfully disagrees. The modification of an order to carry vendor attribution is an abstract concept and the types of data collected (identifying the item and store using a picture) are capable of being collected by a human. The mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. See MPEP 2106.04(a). In response to arguments directed to Desjardins, Examiner notes that while the employment of a machine learning model is recited in dependent claims, the claims do not recite limitations directed to machine learning model efficiencies that reduce storage requirements or preserve task performance over sequential training. Examiner also notes that the claims merely recite what information is associated with the Augmented Reality model, rather than an improved data storage structure or an improved rendering of information on an interface (e.g., examples of how a computer system could be configured to operate differently than generic computing functionality – collection, storage, transmission, display of information). Examiner further notes that several arguments rely on language that is not recited in the claims (e.g., rewriting, embedding, etc.)
Applicant further argues that the ordered combination of elements in claim 1 provides significantly more than any alleged abstract idea because it recites a non-conventional and non-generic arrangement of computer, sensor, database, and network operations. Examiner respectfully disagrees. Selecting a particular type of data to be manipulated, such as selecting information for collection, analysis and display (Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)), is a function that the courts have found to be insignificant extra-solution activity. See MPEP 2106.05(g). Examiner notes, in response to presented arguments, that there is no recitation of an algorithmic determination in the claims, the modification of an outbound purchase request to include vendor information (at this level of breadth) is abstract, the showrooming-driven loss of item-vendor correlation is a common business (abstract) problem that pre-dated online purchasing through personal devices, and the claims do not recite a specific “technical architecture” such as an improved database structure (but rather, recites only what types of collected data are stored together).
Therefore, the rejections of the claims under 35 USC 101 has been upheld.
Applicant’s amendments and associated arguments, filed 1/8/26, with respect to the rejection of the under 35 U.S.C. §102 have been considered but are moot because the arguments do not apply to all of the references being used in the current rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claim 4 limitation “wherein determining the vendor employs a machine learning model that predicts the vendor based on prior transactions associated with the vendor”, reads as an incomplete sentence. It is unclear whether the claim is directed to determining that the vendor employes a machine learning model (wherein the model can be used to make a prediction), or directed to a prediction being made using a machine learning model employed by the vendor. For the purpose of examination, the second interpretation as it is consistent with the disclosure in the specification (e.g., [0006] [0037]).
Claims 11 and 18 recite analogous limitations and are rejected for at least the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-20 are directed to a method (process), a system (machine or manufacture), and non-transitory medium (manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those identified in bold below:
1. A computer-implemented method for correlating consumer items and commercial vendors, the computer-implemented method comprising: capturing, by a system comprising a processor, using at least one sensor of a portable device, an image of an item and a surrounding area of a physical store carrying the item; identifying, by the system, the item in the image; determining, by the system, a vendor associated with the physical store for the item by recognizing the surrounding area in the image and associating the surrounding area with the vendor for the item; obtaining, by the system, fulfillment information for the item from a vendor database; generating, by the system, a virtual model for the vendor for the item, wherein the virtual model includes the image and the fulfillment information for the item; receiving, by the system, via the portable device, a request for the item from a user of the portable device to obtain the item from an online retailer that is not related to the vendor; modifying, by the system, the request for the item by adding an indication of the vendor to the request resulting in a modified request; and transmitting the modified request for the item to a server associated with the online retailer.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than reciting that the method is computer-implemented nothing in the claim element precludes the aforementioned (bolded) steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of a generic computer does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
Claims 8 and 15 recite analogous abstract limitations and therefore recite an abstract idea for the same reasons as those presented above with respect to claim 1.
If the claim recites a judicial exception in step 2A Prong One , the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim recites the additional elements that are underlined below:
1. A computer-implemented method for correlating consumer items and commercial vendors, the computer-implemented method comprising: capturing, by a system comprising a processor, using at least one sensor of a portable device, an image of an item and a surrounding area of a physical store carrying the item; identifying, by the system, the item in the image; determining, by the system, a vendor associated with the physical store for the item by recognizing the surrounding area in the image and associating the surrounding area with the vendor for the item; obtaining, by the system, fulfillment information for the item from a vendor database; generating, by the system, a virtual model for the vendor for the item, wherein the virtual model includes the image and the fulfillment information for the item; receiving, by the system, via the portable device, a request for the item from a user of the portable device to obtain the item from an online retailer that is not related to the vendor; modifying, by the system, the request for the item by adding an indication of the vendor to the request resulting in a modified request; and transmitting the modified request for the item to a server associated with the online retailer.
8. A system comprising: a memory configured to store computer-executable instructions; and a processor configured to execute at least one of the computer-executable instructions…
15. A computer program product for correlating consumer items and commercial vendors, the computer program product comprising at least one more non- transitory computer-readable medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to….
The implementation by the computer and the recited elements of a computer system (processors, memories, etc.), is/are recited at a high level of generality, and, as applied, are a tool used in its ordinary capacity to perform the abstract idea, and therefore amount to “apply it.”
The characterization of the model as virtual and the retailer as an online retailer amounts to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
The capture of an image using at least one sensor of a portable device is recited at a high level of generality (i.e. as a general means of gathering information for use in the following steps), and amounts to mere data gathering, which is a form of extra-solution activity.
The receiving and transmitting functions of the system, the portable device, and the server, are recited at a high level of generality (i.e. as a general means of sharing information), and amounts to mere data sharing, which is a form of extra-solution activity.
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As mentioned above, the implementation by the computer and the recited elements of a computer system (processors, memories, etc.) is/are recited at a high level of generality, and, as applied, are a tool used in its ordinary capacity to perform the abstract idea, and therefore amount to “apply it.” Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
As mentioned above, the characterization of the model as virtual and the retailer as an online retailer amounts to merely indicating a field of use or technological environment in which to apply a judicial exception, which does not amount to significantly more than the exception itself. (see MPEP 2106.05(h)).
As indicated above, the capture of an image using at least one sensor of a portable device amounts to insignificant extra-solution activity. The specification of the application, for example in in paragraph 0033, indicates that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art."). MPEP 2106.05(d)(I)(2).
As indicated above, the receiving and transmitting functions of the system, the portable device, and the server, are recited at a high level of generality (i.e. as a general means of sharing information), and amounts to mere data sharing, which is a form of insignificant extra-solution activity. The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea.
Regarding claims, 2, 9 and 16, the presentation of the model within an augmented reality display merely amounts to post-solution activity when recited at this level of breadth. Examiner notes that there are no limitations directed to the particulars of the “augmented reality” features of the display and as such the display of information itself is generically recited. The display of the model within an augmented reality display merely amounts to post-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function). In addition, see Arrasvuori (20080071559 A1), which in paragraph 0073 discloses the generic nature of augmented reality data being presented in a generic display.
Dependent claims 3-6, 10-13 and 17-20 recite abstract limitations that further characterize the aforementioned abstract idea (receiving requests/location information, modifying requests, associating information, making predictions, monitoring interactions, updating requests, obtaining profiles, displaying information, etc.) and additional elements including (i) obtaining, receiving and transmitting functions (i.e. acquiring information from a device, transmitting information to a server), (ii) the application of machine learning model, (iii) storage of information. With respect to element (i), The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). With respect to element (ii), the machine learning model is recited at a high level of generality and thus amounts to mere instructions to apply an exception using a generic computer component. With respect to element (iii), use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-10, 12-17 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Calman et al. (US 20120229657) in view of Zellner et al (US 20160125491 A1).
With respect to claim 1, Calman teaches:
A computer-implemented method for correlating consumer items and commercial vendors (Calman [0024] Referring now to FIG. 1, a general process flow 100 is provided for providing relationship information between one or more individuals associated with a user and information associated with products, locations, businesses, etc., identified in a captured image. ), the computer-implemented method comprising:
capturing, by a system comprising a processor, using at least one sensor of a portable device, an image of an item and a surrounding area of a physical store carrying the item (Calman Fig. 1, [0024] as represented by block 110, the apparatus is configured to receive information associated with an image. In some embodiments, the image is captured by a mobile device operated by a user; Calman Fig. 3 [0088] Mobile device field of view 340 includes the field of view of mobile device 312. In some embodiments, mobile device field of view 340 may be the field of view of the mobile device's digital camera functionality and in other embodiments, mobile device field of view 340 may be the field of view of the mobile device's digital video recorder functionality. In this embodiment, Object 342, Object 344 and Object 346 are located in mobile device field of view 340. As explained earlier, an object may be a product, business, location, etc. In some embodiments, mobile device user 310 uses mobile device 312 and AR Apparatus 330 to determine if objects 342-346 are related to one or more identified individuals. However, in other embodiments, objects 342-346 may be any object (e.g., product, location, business, etc.) that exists within mobile device field of view 340. Mobile device field of view 340 is not limited to this embodiment including objects 342-346. Mobile device field of view 340 may include an infinite number of objects, where each object may take on an infinite number of forms; Calman [0118] Mobile device 312 includes user input device 440A and display 434. As shown in FIG. 7, display 434 is displaying an image of mobile device field of view 340. In this embodiment of the invention, mobile device 312 is capturing a real-time video stream of mobile device field of view 340..);
identifying, by the system the item in the image; determining, by the system, a vendor associated with the physical store for the item by recognizing the surrounding area in the image and associating the surrounding area with the vendor for the item (Calman 0028] Further regarding block 110, the phrase "information associated with an image" could be any information associated with an image. In some embodiments, the image itself may be information associated with an image. In some embodiments, metadata, which could be decoded into the image or stored elsewhere, could be information associated with an image. In some embodiments, information associated with an image could be the results of any analysis of the image (e.g., image comparison analysis, pattern recognition analysis or image recognition analysis). In another embodiment of the invention, information associated with an image could by the output of any modeling or composite imaging processes that are based in part on the image. In yet another embodiment, information associated with an image could be information concerning the location of an object depicted in an image; Calman [0032] Regarding block 110, after receiving the image, in some embodiments, the apparatus may also identify a product, business, location, etc. (hereinafter referred to as an "object") that is depicted in the image. The phrase "depicts an object" refers to an image that provides any type of representation of an object. For example, in some embodiments, an image may depict an object if the object is visible anywhere in the image. The object may be in the foreground of the image or the object may be in the background of the image. Furthermore, the entire object may be visible in the image or only a portion of the object may be visible in the image; Calmnan [0033] 0033] In some embodiments of the invention where the object is a product, the identifying data may include: the size, shape or color of a product's packaging; a product's logo; the bar code information associated with a product; the ratio of the size of one feature of a product or its packaging to another feature; a product's physical location; or the appearance of a product itself (as opposed to its packaging).; Calman 0034] For a business, the identifying data may be any type of data that would identify a business. In some embodiments, the identifying data may include: the logo of the business, the decor of the business, one or more trademarks associated with the business, etc. For a location, the identifying data is any type of data that would identify a location, e.g., a street sign, a famous landmark, a street number that is visible on a local business or a home, a positioning system device signal that is communicated from the mobile device (e.g., global positioning system (GPS) signal), etc.; see also [0036]-[0038] );
obtaining, by the system, fulfillment information for the item from a vendor database (Calman [0078] In some embodiments, the mobile device and/or the server access one or more databases or datastores (not shown) to search for and/or retrieve information related to the object and/or marker; Calman [0089] In various embodiments, information associated with or related to one or more objects that is retrieved for presentation to a user via the mobile device may be permanently or semi-permanently associated with the object. In other words, the object may be "tagged" with the information. In some embodiments, a location pointer is associated with an object after information is retrieved regarding the object. In this regard, subsequent mobile devices capturing the object for recognition may retrieve the associated information, tags and/or pointers in order to more quickly retrieve information regarding the object… In some embodiments, the information gathered through the recognition and information retrieval process may be posted by the user in association with the object. Such tags and/or postings may be stored in a predetermined memory and/or database for ease of searching and retrieval); and
generating, by the system, a virtual model for the vendor for the item, wherein the virtual model includes the image and the fulfillment information for the item (Calman [0024] In addition, as represented in block 140, the apparatus is configured to present, via the mobile device of the user, information associated with the one or more relationships. An apparatus may be able to execute each of the four blocks of FIG. 1 dynamically in real-time. In some embodiments, "real-time" means instantly or immediately upon a user capturing the image or a user executing an AR function, while in other embodiments "real-time" may mean a short delay of a few seconds (e.g., thirty seconds) or a few minutes (e.g., two minutes); Calman [0052] An indicator as described herein may be identified by an object recognition application or an AR application. An indicator may be any type of indicator that is a distinguishing feature that can be interpreted by the object recognition application or the AR application to identify objects or relationships; [0053] In some embodiments, the indicator is "selectable" and a user may "select" the indicator and retrieve information related to the object or the individual related to the object, including how the individual is related to the object. The information may include any desired information concerning the object or the individual and may range from basic information to greatly detailed information. Alternatively, all or a portion of the indicator may include a hyperlink; [0055] In some embodiments, the indicator is not interactive and simply provides information to the user by displaying information on the display of a mobile device. For example, in some embodiments, the indicator may merely identify an object, just identify the object's name/title, or present brief information about the object, etc., rather than provide extensive detail that requires interaction with the indicator; Calman [0089] In various embodiments, information associated with or related to one or more objects is retrieved for presentation to a user via the mobile device may [see also [0093] disclosing AR application may be local or remote to the image capture device; Fig. 5-8 for characterization of augmented reality display]);
receiving, by the system, via the portable device, a request for the item from a user of the portable device to obtain the item from an online retailer …;
Calman [0080] some embodiments, the indicator may provide an Internet hyperlink to enable the user to obtain further information about the product or enable the user to purchase the product by directing the user to a website where product is offered for sale. Calman [0081] In some embodiments, the user then may purchase the product from the website of the store where the user is currently located, pay for the product online, and then take the product from the store. In other embodiments, the user may purchase the product online from another retailer and then that retailer will ship or deliver the product to the user.
transmitting the … request for the item to a server associated with the online retailer.
Calman [0081] In other embodiments of the invention, the user selects the indicator associated with the product to access a hyperlink that enables the user to purchase the product from a website. In some embodiments, the user then may purchase the product from the website of the store where the user is currently located, pay for the product online, and then take the product from the store. In other embodiments, the user may purchase the product online from another retailer and then that retailer will ship or deliver the product to the user.
Calman, as shown above, discloses a request for an identified item to an online retailer, but does not explicitly disclose that the retailer is not related to the vendor, the modification of said request with vendor information or the transmission of said modified request as claimed. Zellner is directed to facilitating purchase transactions in a showroom. Zellner discloses:
receiving, by the system, via the portable device, a request for the item from a user of the portable device to obtain the item from an online retailer that is not related to the vendor; modifying, by the system, the request for the item by adding an indication of the vendor to the request resulting in a modified request; and transmitting the modified request for the item to a server associated with the online retailer.
Zellner [0033] System 300 provides for the showroom engine 210 detecting when a user in a showroom is seeking to purchase a product, obtaining user data in response to the detection, analyzing the user data, and selecting interaction data based on the analysis which can assist in completing a purchase transaction of a product. .. The interaction data … is then forwarded to an online merchant so that the online merchant can later complete a sale of the product with the user.
Zellner [0036] In one embodiment, the showroom engine 210 can obtain attributes of a user, determine what the user is looking at (what and where), determine what the store has available to sell the user, determine idle time while walking aisle of products (e.g., utilized as a showrooming predictor), determine if user is active on a communication device (e.g., a smart phone), determine what activities are being executed on the communication device. (see also [0023] such as a communication session of the communication device 116 with a content source (e.g., a content download) that is related to the product, including viewing a consumer reports website for a particular product 125 that the user is in proximity to in the showroom 102. For example, the server 130 can request and obtain a list of recently accessed websites from the communication device 116. More specific information can also be requested by the server 130 of the communication device 116, including recent searches via a search engine accessed by the communication device.)
Zellner [0039] In one embodiment, a buy decision on behalf of the user can be detected or otherwise determined. If the purchase is from an online merchant, the showroom 102 can share in revenue of the purchase. (see also [0031] In one embodiment, the server 130 can determine that the user 105 has not purchased the product 125 in the showroom 102. The interaction data can be provided to a computing device 150 of an online entity (which may be a different entity than manages the server 130 or can be the same entity) to enable or otherwise facilitate transacting with the user 105 for the purchase of the product 125. In one embodiment, the server 130 can determine that the user 105 has purchased the product 125 from the online entity and can engage in revenue sharing with the online entity according to a purchase transaction for the product. In this example, the showroom 102 can be managed by a first entity that is different from the online entity.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Zellner to Calman would have yielded predictable results and resulted in an improved system that would facilitate revenue sharing between all entities involved in the purchasing process (product exploration, search and acquisition).
With respect to claim 2, the combination of Calman and Zellner teaches the limitations of claim 1 and further discloses:
wherein the virtual model is an augmented reality model presented on the portable device (Calman [0052] An indicator as described herein may be identified by an object recognition application or an AR application. An indicator may be any type of indicator that is a distinguishing feature that can be interpreted by the object recognition application or the AR application to identify objects or relationships..; Calman [0066] In such embodiments, the AR application may be configured to alert the user that a user-selected object has been identified to be related to an individual associated with the user; Calman [0093] The processor 410 may also be capable of operating applications, such as an AR application 421. The AR application 421 may be downloaded from a server and stored in the memory 420 of the mobile device 312. In other embodiments, AR application 421 may be pre-installed on memory 420. AR application may include computer-readable code that when executed by processor 410 may provide the AR functionality described herein with regards to mobile device 312; Calman [0107] FIG. 5 also illustrates indicators 520, which in this embodiment are superimposed over the real-time video stream of mobile device field of view 340, which is displayed on mobile device display 434. The indicator may be superimposed using either an AR application or an object recognition application; Calman [0113] In this way, the intelligent software may automatically order the product after the user indicates he would like to purchase the product using the AR application. The product is put into a purchasing queue and the intelligent software agent provides for the remaining transaction requirements.).
With respect to claim 3, the combination of Calman and Zellner teaches the limitations of claim 1 and further discloses:
obtaining, by the system, location data for the surrounding area from the portable device; and associating, by the system, the surrounding area with the vendor for the item when the location data matches a vendor location of the physical store (Calman [0120] FIG. 7 also illustrates an indicator 520. Here, a system configured to perform the process described in FIGS. 1 and 2 automatically determines that the object depicts an identified location. In some embodiments, the system may makes this location determination by identifying from the image a street sign, or a famous landmark, or a street number that is visible on a local business or a home, etc. In other embodiments, a system makes this location determination using one or more other location determining mechanisms. For example, the system may make this location determination by communicating with a global positioning system (GPS) satellite that receives a signal from a positioning system device located in the mobile device 312. Also as explained with respect to FIG. 4 above, in alternate embodiments, a system makes this location determination by determining a network address associated with the mobile device 312 or by identifying a location of a cell site (or cell tower) that is located closest to the mobile device 312. In some embodiments, one or more of the above location determining mechanisms may be used in combination with each other in order to confirm the accuracy of the location identified by one of the location determining mechanisms; Calman [0089] In various embodiments, information associated with or related to one or more objects that is retrieved for presentation to a user via the mobile device may be permanently or semi-permanently associated with the object. In other words, the object may be "tagged" with the information. In some embodiments, a location pointer is associated with an object after information is retrieved regarding the object. In this regard, subsequent mobile devices capturing the object for recognition may retrieve the associated information, tags and/or pointers in order to more quickly retrieve information regarding the object…Such tags and/or postings may be stored in a predetermined memory and/or database for ease of searching and retrieval).
With respect to claim 5, the combination of Calman and Zellner teaches the limitations of claim 1 and further discloses:
modifying, by the system, based on additional captured images from the portable device, the virtual model in real-time as the user moves through the physical store (Calman [0105] Mobile device 312 includes user input device 440A and display 434. As shown in FIG. 5, display 434 is displaying an image of mobile device field of view 340. In this embodiment of the invention, mobile device 312 is capturing a real-time video stream of mobile device field of view 340.; [0107] FIG. 5 also illustrates indicators 520, which in this embodiment are superimposed over the real-time video stream of mobile device field of view 340, which is displayed on mobile device display 434; [0118] Mobile device 312 includes user input device 440A and display 434. As shown in FIG. 7, display 434 is displaying an image of mobile device field of view 340. In this embodiment of the invention, mobile device 312 is capturing a real-time video stream of mobile device field of view 340. See also Calman [0044] –[0045] In some embodiments, apparatus may receive data through an API. In this way, the data may be stored in a separate API and be implemented by request from the mobile device and/or server accesses another application by way of an API.; Calman [0047] Regarding block 130, the apparatus having the process flow 100 may use any means to determine that an individual determined at block 120 is related to an object identified at block 110. As used herein, the phrase "related to" may mean that an individual has engaged in a transaction associated with the object (e.g., purchase or return of a product), has written something about the object (e.g., blog post or message on a social network about a product, business, location, etc.), has previously visited the object (e.g., a location or a business), or the like; Calman [0080] In some embodiments, the indicator may provide an Internet hyperlink to enable the user to obtain further information about the product or enable the user to purchase the product by directing the user to a website where product is offered for sale; Calman [0081] in some embodiments, the user then may purchase the product from the website of the store where the user is currently located, pay for the product online, and then take the product from the store)).
With respect to claim 6, the combination of Calman and Zellner teaches the limitations of claim 1 and further discloses:
storing the virtual model for the vendor for the item in the vendor database (Calman [0039] In yet another embodiment of block 110, the information associated with the image may match one or more pieces of identifying data, such that the apparatus determines that the image depicts more than one object. In such instances, the user may be presented with multiple candidate identifications and may opt to choose the appropriate identification or input a different identification. …. Upon input by the user identifying the object, the apparatus may "learn" from the input and store additional identifying data in order to avoid multiple identification candidates for the same object in future identifications.; Calman [0089] In various embodiments, information associated with or related to one or more objects that is retrieved for presentation to a user via the mobile device may be permanently or semi-permanently associated with the object. In other words, the object may be "tagged" with the information. In some embodiments, a location pointer is associated with an object after information is retrieved regarding the object. In this regard, subsequent mobile devices capturing the object for recognition may retrieve the associated information, tags and/or pointers in order to more quickly retrieve information regarding the object… In some embodiments, the information gathered through the recognition and information retrieval process may be posted by the user in association with the object. Such tags and/or postings may be stored in a predetermined memory and/or database for ease of searching and retrieval.
With respect to claim 7, the combination of Calman and Zellner teaches the limitations of claim 1 and further discloses:
obtaining, by the system, a profile of the user; and modifying, by the system, the fulfillment information for the item based on the profile of the user (Zellner [0026] In one embodiment, the server 130 can obtain user data of the user 105 in response to determining that the user is seeking to purchase the product (e.g., based on the techniques described herein including showroom location, eye-tracking, device activity, and so forth). The obtaining of the user data can include identifying the user 105, such as based on communications with the communication device 116 of the user, image pattern recognition of images captured of the user and/or other identification techniques. The user data can be of various types including user preferences (e.g., according to user input such as in a user profile that is accessed by the server 130 via a request provided to the communication device 116), a transaction history of the user (such as obtained from records of the showroom, records of other showrooms, or from other sales sources), a media consumption history of the user (such as obtained from a service provider of the user), demographics of the user, or a combination thereof. … The sources can be managed by the same entity managing the server 130 and/or can be managed by other entities that are different from the entity managing the server 130.; [0027] In one embodiment, the server 130 can analyze the user data and can select interaction data from among a group of interaction data according to the analysis. … The interaction data can be a customized guide for facilitating a sale to the user 105.) (see also Calman [0080] - [0081], [0113])
One of ordinary skill in the art at the time of filing would have recognized that applying the profiling technique of Zellner to customer profiling of Calman would have yielded predictable results and resulted in an improved system that would improve recognition of product interest and increase the likelihood of facilitating product purchase.
With respect to claims 8-10, 12-14, the limitations are analogous to the limitations of claims 1-3, 5-7, and are thereby rejected for the same reasons identified above with respect to claims 1-3, 5-7. In addition, Calman further teaches: A comprising: a memory configured to store computer-executable instructions; and a processor configured to execute at least one of the computer-executable instructions [to execute the steps identified above with respect to the method claims] (see Fig. 3, [0082]-[0089], Fig. 4 [0090]-[0103])
With respect to claims 15-17 and 19-20, the limitations are analogous to the limitations of claims 1-3, 5-7, and are thereby rejected for the same reasons identified above with respect to claims 1-3, 5-7. In addition, Calman further teaches: A computer program product for correlating consumer items and commercial vendors, the computer program product comprising at least one non- transitory computer-readable medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor [to execute the steps identified above with respect to the method claims] (see Fig. 3, [0082]-[0089], Fig. 4 [0090]-[0103])
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Calman et al. (US 20120229657),in view of Zellner (US 20160125491 A1) as applied above, and further in view of Systrom (20140279039 A1).
With respect to claim 4, the combination of Calman and Zellner teaches the limitations of claim 3 and further discloses:
wherein determining the vendor employs a machine learning model that [identifies] the vendor based on prior transactions associated with the vendor (Calman [0047] Regarding block 130, the apparatus having the process flow 100 may use any means to determine that an individual determined at block 120 is related to an object identified at block 110. As used herein, the phrase "related to" may mean that an individual has engaged in a transaction associated with the object (e.g., purchase or return of a product), has written something about the object (e.g., blog post or message on a social network about a product, business, location, etc.), has previously visited the object (e.g., a location or a business), or the like. The phrase "related to" should be afforded the broadest interpretation possible and may capture other embodiments not described herein. In some embodiments, the apparatus may utilize one or more methods, such as pattern recognition algorithms, to analyze first information associated with an image, analyze second information associated with one or more individuals, and compare the first information with the second information. If the first information associated with the image matches second information associated with one or more individuals, either exactly or with a certain degree of confidence, then the apparatus determines that a relationship exists between an object and an individual.; Calman [0048] In some embodiments, the apparatus may use comparison or pattern recognition algorithms such as decision trees, logistic regression, Bayes classifiers, support vector machines, kernel estimation, perceptrons, clustering algorithms, regression algorithms, categorical sequence labeling algorithms, real-valued sequence labeling algorithms, parsing algorithms, general algorithms for predicting arbitrarily-structured labels such as Bayesian networks and Markov random fields, ensemble learning algorithms such as bootstrap aggregating, boosting, ensemble averaging, combinations thereof, and the like to determine that an individual determined at block 120 is related to an object identified at block 110. ; Calman [0050] Upon input by the user identifying the relationship, the apparatus may "learn" from the input and store additional data in order to avoid multiple identification candidates (i.e., relationships) for the same object in future identifications, or present the multiple identification candidates in an order that suits the user's interests (e.g., list transaction relationships higher on the list and messaging relationships lower on the list); Calman [0120] FIG. 7 also illustrates an indicator 520. Here, a system configured to perform the process described in FIGS. 1 and 2 automatically determines that the object depicts an identified location. In some embodiments, the system may makes this location determination by identifying from the image a street sign, or a famous landmark, or a street number that is visible on a local business or a home, etc. In other embodiments, a system makes this location determination using one or more other location determining mechanisms. For example, the system may make this location determination by communicating with a global positioning system (GPS) satellite that receives a signal from a positioning system device located in the mobile device 312) (Zellner [0044] In one or more embodiments, the showroom engine 210 can learn from the interaction with the user and use the learned data to facilitate subsequent purchases of products. In one embodiment, the learned data can be images and/or audio captured during the interaction. In another embodiment, the learned data can be other products viewed by the user. As an example, negotiation information such as a user's bid for a product, can be stored (e.g., in database 135) so that subsequent negotiations are adjusted based on historical bid data of the user; Zellner [0063] the user data can include user preferences according to user input, a transaction history of the user, a media consumption history of the user, demographics of the user, or a combination thereof.)
The combination of Calman and Zellner establish the identification of vendors associated with user transactions using learning-based models, which strongly suggest making a “prediction” using a learning-based model. Systrom more clearly establishes transaction data can be used to predict the vendor (Systrom [0033] Block S120 can also implement supervised or semi-supervised machine learning techniques, such as by augmenting a database of images with automatically-generated tags confirmed by users or images with manually-entered tags in order to improve the object image detection algorithm.; [0038] disclosing selecting the merchant from a set of suitable online or brick-and- mortar stores based on …a user's transaction history (e.g., has the user previously shopped with this merchant?), … a user location (e.g., GPS location data of a smartphone associated with the user and that is physically nearby a particular brick-and-mortar merchant), etc.)
One of ordinary skill in the art would have recognized that applying data analysis of Systrom to the teaching of Calman and Zellner would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate analysis that associates locations, transactions and vendors to the conveyance of information to users through augmented interfaces, which improves the interface between buyers and sellers through improved, targeted content generation.
With respect to claim 11, the limitations are analogous to the limitations of claims 4, and are thereby rejected for the same reasons identified above with respect to claim 4.
With respect to claim 18, the limitations are analogous to the limitations of claim 4, and are thereby rejected for the same reasons identified above with respect to claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Muramatsu (20180089725 A1) disclosing systems for detecting customer interactions with products in a physical store for generation of a virtual cart
Signorelli et al. (20140214547 A1), disclosing systems and methods for augmented retail reality.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Trammell whose telephone number is 571-272-6712. The examiner can normally be reached Monday - Friday 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at (571) 272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABBY J FLYNN/ Primary Patent Examiner, Art Unit 3663
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